Network of ’Abd Allah b. As’ad

# Here we are importing our R libraries that we will be using
# for the Arabic Papyri Network Project

library('igraph')

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

library('visNetwork')

## 
## Attaching package: 'visNetwork'

## The following object is masked from 'package:igraph':
## 
##     %>%

Importing our data

# here we are importing our csv files that have our papyri data
nodes <- read.csv('nodes.csv', header = T, as.is = T)
edges <- read.csv('edges.csv', header = T, as.is = T)

#This creates our base network and dumps our node and edge data into one place
net <-graph_from_data_frame(d = edges, vertices = nodes, directed = TRUE)

Print our Nodes

V(net)

## + 36/36 vertices, named:
##  [1] Abd Allah b. As'ad       b. Abi Muslim           
##  [3] Yazid b. 'Ali            Khalid                  
##  [5] Peter the Symmachos      Juzayy                  
##  [7] Yazid                    Anonymous Symmachos     
##  [9] Rashid                   People of Narmuda       
## [11] Qarina                   Anonymous Amir          
## [13] Najid b. Muslim          Abu (missing)           
## [15] Awn b. Nafi'             so and so son of ....man
## [17] two sons of Da'ud        Jarid b. As'ad          
## [19] George the Symmachos     Abu Dawud               
## + ... omitted several vertices

Print our Edges

E(net)

## + 78/78 edges (vertex names):
##  [1] Najid b. Muslim   ->Abd Allah b. As'ad
##  [2] Anonymous Amir    ->Najid b. Muslim   
##  [3] Yazid b. 'Ali     ->Najid b. Muslim   
##  [4] Najid b. Muslim   ->Abd Allah b. As'ad
##  [5] Anonymous Amir    ->Najid b. Muslim   
##  [6] Najid b. Muslim   ->Abd Allah b. As'ad
##  [7] Anonymous Amir    ->Najid b. Muslim   
##  [8] Najid b. Muslim   ->Abd Allah b. As'ad
##  [9] Anonymous Amir    ->Najid b. Muslim   
## [10] Najid b. Muslim   ->Abd Allah b. As'ad
## + ... omitted several edges

Attributes

# With R we can assign attributes to your nodes and edges.
# To make sure these attributes were properly assigned, we can
# Print them just like our edges and nodes

E(net)$Citation

##  [1] "P.Mich.inv.5558(1)"        "P.Mich.inv.5558(1)"       
##  [3] "P.Mich.inv.5558(1)"        "P.Mich.inv. 5629"         
##  [5] "P.Vindob. A P 11045"       "P.Vindob. A P 11045"      
##  [7] "P.Mich.inv. 5624"          "P.Mich.inv. 5624"         
##  [9] "P.Mich.inv. 5578(1)"       "P.Mich.inv. 5578(1)"      
## [11] "P.Vindob. A P 11253"       "P.Vindob. A P 11253"      
## [13] "P.Mich.inv. 5627"          "P.Mich.inv. 5627"         
## [15] "P.Mich.inv. 5558(2)"       "P.Mich.inv 5578(3)"       
## [17] "P.Mich.inv 5578(3)"        "P.Mich.inv 5578(5)a"      
## [19] "P.Mich.inv 5578(5)b"       "P.Mich.inv 5578(5)b"      
## [21] "P.Mich.inv 5578(7)"        "P.Mich.inv 5578(6)"       
## [23] "P.Mich.inv 5578(6)"        "P.Mich.inv 5610"          
## [25] "P.Mich.inv 5610"           "P.Mich.inv 5610"          
## [27] "P.Mich.inv 5613(b)"        "P.Mich.inv 5613(b)"       
## [29] "P.Mich.inv 5613(b)"        "P.Mich.inv 5613(b)"       
## [31] "P.Mich.inv 5613(b)"        "P.Mich.inv 5613(b)"       
## [33] "P.Mich.inv 5625(a)"        "P.Mich.inv 5625(a)"       
## [35] "P.Mich.inv 5625(a)"        "P.Mich.inv 5625(a)"       
## [37] "P.Mich.inv 5625(a)"        "P.Mich.inv 5625(a)"       
## [39] "P.Mich.inv 5625(a)"        "P.Mich.inv 5620"          
## [41] "P.Mich.inv 5620"           "P.Mich.inv 5620"          
## [43] "P.Princeton AM 133995(8)"  "P.Mich.inv 5619"          
## [45] "P.Mich.inv 5619"           "P.Vindob. A P 11252"      
## [47] "P.Vindob. A P 11252"       "P.Vindob. A P 11252"      
## [49] "P.Mich.inv 5613(a)"        "P.Mich.inv 5613(a)"       
## [51] "P.Mich.inv 5632"           "P.Mich.inv 5578(9)"       
## [53] "P.Mich.inv 5578(9)"        "P.Mich.inv 5578(9)"       
## [55] "P.Mich.inv 5578(9)"        "P.Mich.inv 5578(9)"       
## [57] "P.Mich.inv 5622"           "P.Mich.inv 5622"          
## [59] "P.Mich.inv 5609"           "P.Mich.inv 5609"          
## [61] "P.Mich.inv 5609"           "P.Mich.inv 5609"          
## [63] "P.Mich.inv 5609"           "P.Mich.inv 5609"          
## [65] "P.Mich.inv 5621"           "P.Mich.inv 5623"          
## [67] "P.Mich.inv 5623"           "P.Mich.inv 5623"          
## [69] "P.Mich.inv 5626(b)"        "P.Mich.inv 5631"          
## [71] "Chester Beatty Pp 8"       "Mich. Pap. A 847"         
## [73] "P.Mich.inv. 5578(8)"       "P.Vindob. A P 25"         
## [75] "P.Vindob. A P 1874"        "P.Vindob. A P 1875 side A"
## [77] "P.Vindob. A P 1875 side A" "P.Vindob. A P 1875 verso"

# This prints the rank of our Nodes
V(net)$Rank

##  [1] "Official"      "Official"      "Treasurer"     "Resident"     
##  [5] "Symmachos"     "Sahib al-Ma'a" "Official"      "Symmachos"    
##  [9] "Sahib al-Ma'a" "Village"       "Sahib"         "Amir"         
## [13] "Sahib"         "Official"      "Official"      "Resident"     
## [17] "Resident"      "Sahib"         "Symmachos"     "Symmachos"    
## [21] "Official"      "Official"      "Freedman"      "Official"     
## [25] "Resident"      "Resident"      "Friend"        "Oil Man"      
## [29] "Official"      "mawla"         "Official"      "Official"     
## [33] "Official"      "Official"      "Scribe"        "Official"

Plotting our network

# R has an integrated plotting library that is pretty intuitive. 
# This is different from Python where we had to import the matplotlib
# Library in tandem with other Python libraries

plot(net, edge.arrow.size = .4,
          edge.curved =.1,
          vertex.label.color = "black",
          layout = layout_components) #There are other layout options that I will show next

# We can also produce multiple network visualizations as rows and columns

par(mfrow=c(2,2), mar=c(0,0,0,0)) # plots 4 figures as 2 x 2

plot(net, edge.arrow.size = .4, edge.curved =.1,
     vertex.label.color = "black", layout = layout_with_fr)
plot(net, edge.arrow.size = .4, edge.curved =.1,
     vertex.label.color = "black", layout = layout_on_sphere)
plot(net, edge.arrow.size = .4, edge.curved =.1,
     vertex.label.color = "black", layout = layout_as_star)
plot(net, edge.arrow.size = .4, edge.curved =.1,
     vertex.label.color = "black", layout = layout_on_grid)

# Let's add some more!

layouts <- grep("^layout_", ls("package:igraph"), value=TRUE)[-1]
layouts <- layouts[!grepl("bipartite|merge|norm|sugiyama|tree", layouts)]

par(mfrow=c(3,3), mar=c(1,1,1,1))
for (layout in layouts) {
  print(layout)
  l <-do.call(layout, list(net))
    plot(net, vertex.label = NA, edge.arrow.mode=0, layout=l, main=layout)
  #I removed the node label here to make it not as messy. 
      }

## [1] "layout_as_star"

## [1] "layout_components"

## [1] "layout_in_circle"

## [1] "layout_nicely"

## [1] "layout_on_grid"

## [1] "layout_on_sphere"

## [1] "layout_randomly"

## [1] "layout_with_dh"

## [1] "layout_with_drl"

## [1] "layout_with_fr"

## [1] "layout_with_gem"

## [1] "layout_with_graphopt"

## [1] "layout_with_kk"

## [1] "layout_with_lgl"

## [1] "layout_with_mds"

Visualizing Statistics!

  deg <- degree(net, mode="all") #defining our "Kevin Bacon" factor
  # We can make a histogram for our degrees
  deg.dist <- degree_distribution(net, cumulative=T, mode="all")
  plot(x=0:max(deg), y=1-deg.dist, pch=19, cex=1.2, col="blue",
       xlab="Degree", ylab="Cumulative Frequency")

  ceb <-cluster_edge_betweenness(net)
  # Creating a Dendrogram
  dendPlot(ceb, mode="hclust")

  # Let's see the same dendrogram, but
  # highlighted in network view
plot(ceb, net,edge.arrow.size = .4,
          edge.curved =.1,
          vertex.label.color = "black",
          layout = layout_components)

Using visNetwork for Visualization

# With visNetwork, we can great an interactive
# visualization of our Papyri network
# that can allow the user to query different
# attributes related to the papyri to highlight
# the different relationships between social actors

# Here we import our network from our igraph library
# and create a new network in the visNetwork library
data <- toVisNetworkData(net)
nodes2 = data$nodes
edges2 = data$edges

# Basic command for visualization. Play with it!!
visNetwork(nodes2, edges2)

# Let's add some bells and whistles to this 
# visualization, and try to get the most out of it.

nodes2$shape <- "dot"
nodes2$shadow <- TRUE
nodes2$borderWidth <- 2
edges2$color <- "gray"
edges2$arrows <- "to"
edges2$smooth <- TRUE
edges2$shadow <- FALSE
edges2$title <- paste("Citation", edges2$Citation) #This creates a popup with the citation

visNetwork(nodes2, edges2, width = "100%", height="700px") %>%
  visEdges(shadow=c(FALSE),
           arrows=c("to")
           )%>%
  visOptions(highlightNearest=TRUE,
             selectedBy = "Rank",
             nodesIdSelection = TRUE) %>%
  visInteraction(navigationButtons = TRUE)

# Sadly, I haven't been able to figure out how to add
# multiple attributes to be searched and/or highlighted, etc.
# so, let's change things up a bit from the last visualization.

nodes2$shape <- "dot"
nodes2$shadow <- TRUE
nodes2$borderWidth <- 2
nodes2$color <-"tomato"
edges2$color <- "gray30"
edges2$arrows <- "to"
edges2$smooth <- TRUE
edges2$shadow <- FALSE
edges2$title <- paste("Letter Type:", edges2$Letter.Type) #This creates a popup with the Type of Letter

visNetwork(nodes2, edges2, width = "100%", height="700px") %>%
  visEdges(shadow=c(FALSE),
           arrows=c("to")
           )%>%
  visOptions(highlightNearest=TRUE,
             selectedBy = "Location", # changed this to Location
             nodesIdSelection = TRUE) %>%
  visInteraction(navigationButtons = TRUE)

Spatial Networks with R

# This is an example of the geospatial capabilities with R
# For the following examples, I'd like to thank 
# Katya Ognyanova's online documentation
# http://kateto.net/network-visualization
#
# I wanted to make a network map of this dossier over Egypt
# But I couldn't find the documentation in time to make such a map
# Instead, here's an example from the network documentation
# just to show that it is possible. 
library('maps')
library('geosphere')

## Loading required package: sp

par(mfrow=c(2,2), mar=c(0,0,0,0))

map('usa', col='tomato', border='gray10', fill=TRUE, bg='gray30')
map('usa', col='orange', border='gray10', fill=TRUE, bg='gray30')
map('county', col='palegreen', border='gray10', fill=TRUE, bg='gray30')
map('world', col='skyblue', border='gray10', fill=TRUE, bg='gray30')